Short-term local prediction of wind speed and wind power based on singular spectrum analysis and locality-sensitive hashing

With the growing penetration of wind power in power systems, more accurate prediction of wind speed and wind power is required for real-time scheduling and operation. In this paper, a novel forecast model for short-term prediction of wind speed and wind power is proposed, which is based on singular spectrum analysis (SSA) and locality-sensitive hashing (LSH). To deal with the impact of high volatility of the original time series, SSA is applied to decompose it into two components: the mean trend, which represents the mean tendency of the original time series, and the fluctuation component, which reveals the stochastic characteristics. Both components are reconstructed in a phase space to obtain mean trend segments and fluctuation component segments. After that, LSH is utilized to select similar segments of the mean trend segments, which are then employed in local forecasting, so that the accuracy and efficiency of prediction can be enhanced. Finally, support vector regression is adopted for prediction, where the training input is the synthesis of the similar mean trend segments and the corresponding fluctuation component segments. Simulation studies are conducted on wind speed and wind power time series from four databases, and the final results demonstrate that the proposed model is more accurate and stable in comparison with other models.

[1]  A. Llombart,et al.  Statistical Analysis of Wind Power Forecast Error , 2008, IEEE Transactions on Power Systems.

[2]  Zhang Yang,et al.  Electricity price forecasting by a hybrid model, combining wavelet transform, ARMA and kernel-based extreme learning machine methods , 2017 .

[3]  Yachao Zhang,et al.  Deterministic and probabilistic interval prediction for short-term wind power generation based on variational mode decomposition and machine learning methods , 2016 .

[4]  M. S. Li,et al.  Support vector regression-based short-term wind power prediction with false neighbours filtered , 2013, 2013 International Conference on Renewable Energy Research and Applications (ICRERA).

[5]  T. Y. Ji,et al.  Multistep Wind Power Forecast Using Mean Trend Detector and Mathematical Morphology-Based Local Predictor , 2015, IEEE Transactions on Sustainable Energy.

[6]  N. Bigdeli,et al.  Data analysis and short term load forecasting in Iran electricity market using singular spectral analysis (SSA) , 2011 .

[7]  Huchuan Lu,et al.  Combining motion and appearance cues for anomaly detection , 2016, Pattern Recognit..

[8]  Yusuf Yaslan,et al.  Empirical mode decomposition based denoising method with support vector regression for time series prediction: A case study for electricity load forecasting , 2017 .

[9]  Robert P. Broadwater,et al.  Current status and future advances for wind speed and power forecasting , 2014 .

[10]  Jun Wang,et al.  Forecasting stochastic neural network based on financial empirical mode decomposition , 2017, Neural Networks.

[11]  Xiaobo Zhang,et al.  Short-term electric load forecasting based on singular spectrum analysis and support vector machine optimized by Cuckoo search algorithm , 2017 .

[12]  Antonio Piccolo,et al.  Artificial Neural Network Application in Wind Forecasting: an One-Hour-Ahead Wind Speed Prediction , 2016 .

[13]  Jie Zhang,et al.  A data-driven multi-model methodology with deep feature selection for short-term wind forecasting , 2017 .

[14]  Yitao Liu,et al.  Deep belief network based deterministic and probabilistic wind speed forecasting approach , 2016 .

[15]  Q. Henry Wu,et al.  Local prediction of non-linear time series using support vector regression , 2008, Pattern Recognit..

[16]  Huchuan Lu,et al.  Video anomaly detection based on locality sensitive hashing filters , 2016, Pattern Recognit..

[17]  Joao P. S. Catalao,et al.  Short-term wind power forecasting using adaptive neuro-fuzzy inference system combined with evolutionary particle swarm optimization, wavelet transform and mutual information , 2015 .

[18]  Hüseyin Akçay,et al.  Short-term wind speed forecasting by spectral analysis from long-term observations with missing values , 2017 .

[19]  Haikun Wei,et al.  A Gaussian process regression based hybrid approach for short-term wind speed prediction , 2016 .

[20]  A. Gastli,et al.  Review of the use of Numerical Weather Prediction (NWP) Models for wind energy assessment , 2010 .

[21]  Julian Meng,et al.  A Neural Network Approach to Multi-step-ahead, Short-Term Wind Speed Forecasting , 2013, 2013 12th International Conference on Machine Learning and Applications.

[22]  Jing Zhao,et al.  Multi-step wind speed and power forecasts based on a WRF simulation and an optimized association method , 2017 .

[23]  T. Y. Ji,et al.  Multi-step wind power forecast based on similar segments extracted by mathematical morphology , 2014, 2014 IEEE PES Asia-Pacific Power and Energy Engineering Conference (APPEEC).

[24]  J. Torres,et al.  Forecast of hourly average wind speed with ARMA models in Navarre (Spain) , 2005 .